A Speech Intelligibility Estimation Method Based on Hidden Markov Model

This paper proposes a speech intelligibility estimation method based on hidden Markov model (HMM) that is widely used for speech recognition. The HMM-based method is a kind of non-intrusive speech quality measurement, which means it operates without a reference speech signal. The log-likelihood score of HMM is converted to a normalized intelligibility score. We estimate the speech intelligibility of standard digital speech coders. The experimental results show that the proposed HMM-based method gives improved performance than the conventional non-intrusive speech intelligibility evaluation tool

Speaker Re-identification with Speaker Dependent Speech Enhancement

While the use of deep neural networks has significantly boosted speaker recognition performance, it is still challenging to separate speakers in poor acoustic environments. Here speech enhancement methods have traditionally allowed improved performance. The recent works have shown that adapting speech enhancement can lead to further gains. This paper introduces a novel approach that cascades speech enhancement and speaker recognition. In the first step, a speaker embedding vector is generated , which is used in the second step to enhance the speech quality and re-identify the speakers. Models are trained in an integrated framework with joint optimisation. The proposed approach is evaluated using the Voxceleb1 dataset, which aims to assess speaker recognition in real world situations. In addition three types of noise at different signal-noise-ratios were added for this work. The obtained results show that the proposed approach using speaker dependent speech enhancement can yield better speaker recognition and speech enhancement performances than two baselines in various noise conditions.Comment: Acceptted for presentation at Interspeech202